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In the process of image segmentation based on clustering, it is necessary to manually determine the number of clusters segmented, which makes the image automation less. A fuzzy clustering algorithm is proposed to automatically determine the number of clusters. First, the similarity matrix is constructed using the features of the extracted image, and the number of clusters is determined using the AP clustering algorithm. Then, the obtained number of clusters is input to the FCM algorithm, and the clustering center is optimized using the firefly algorithm. Finally, a simulation experiment was conducted. The experimental results show that the improved FCM algorithm has a good effect and can realize automatic image segmentation.

Objective: To design the lip-color features and classification model and provide an automatic, quantitative method based on the lip detection in facial image. Methods: In this paper, We adopted the lip segmentation algorithm based on the three-dimensional mixture Skin Gaussian Model and color classification in SVM to solve this problem. Specifically, we used the GMM based iterations to confirm skin-color pixels that belong to the skin area. In the process of optimization, we extracted two-dimensional GMM with five parameters as the characteristic to obtain the main color and secondary color to extract and classify the features of the 877 lip images. Consequence: The accuracy and stability of classification method was successfully proved. Conclusion: Firstly, We proposed a Skin Gaussian mixture Model that can be used for the robust and accurate segmentation of facial skin in a probabilistic manner. Secondly, we extracted five parameters from the two-dimensional GMM of the a, b component in the laboratory space as the characteristic of each picture. Finally, we classified the lips through the SVM classifier. As for the lips with mixed color, the method we proposed can have a good performance in identifying the secondary colors, thereby improving the identification precision of the image segmentation and color classification of the lip.

The health management service mode in traditional medicine for the public has attracted more and more attentions in recent years. With the development of large number of health related apps and devices, large quantities of valuable health data can be transmitted and collected through electronic devices such as smart phones. Accordingly, the quantization and digitization of the information in the four diagnostic methods in traditional Chinese medicine have played a more important role at present. In which the inspection diagnosis technology in traditional Chinese medicine can conduct effective diagnosis on health conditions by combining with modern technologies at the premise of reserving the features of traditional medicine. Therefore, the quantization of inspection diagnosis information is especially important. This article mainly introduces the method of tongue diagnosis information quantization in quantization of inspection diagnosis information.